Complexity Science as a Framework
summary
Argues that complex systems are nonlinear, nonstationary, non-Gaussian and far from equilibrium and that emergence makes "more is different" (Phil Anderson 1972) unavoidable; uses the Game of Life and simulations on adjacency matrices to show unpredictability from known rules; points to the 1998 Watts–Strogatz small-world model to explain six degrees of separation; proposes a new statistical mechanics for complex systems and shows via network simulations that modularity prolongs epidemics, letting low-infectious diseases persist.
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chapters (16)
- 0:00Intro
- 4:17What isn't Complex ?
- 6:24What is Complex ?
- 9:09Emergence: The Invisible
- 14:04A crucible of complexity
- 17:33Example: Why statistical
- 26:59What is a network?
- 30:54"It's a small world": The Milgram Experiment
- 39:20Example: small-world networks
- 42:02Do small-world networks occur in real life
- 43:39Intra-Cellular Signaling Network
- 44:52Signal Transduction: A Rube Goldberg Contraption
- 46:53Can a biologist fix a radio ?
- 49:10One approach (Motif analysis)
- 49:47Another approach (Modular analysis)
- 50:11Modules: A mesoscopic organizational principle of networks
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